{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 2: Naive Bayes [20 marks]\n",
    "\n",
    "Student Name:ZHUOYA ZHOU\n",
    "\n",
    "\n",
    "## General info\n",
    "\n",
    "<b>Due date</b>: Friday, 2 September 2022, 5pm\n",
    "\n",
    "<b>Submission method</b>: Canvas submission\n",
    "\n",
    "<b>Submission materials</b>: completed copy of this iPython notebook\n",
    "\n",
    "<b>Late submissions</b>: -10% per day up to 5 days (both weekdays and weekends count)\n",
    "<ul>\n",
    "    <li>one day late, -2.0;</li>\n",
    "    <li>two days late, -4.0;</li>\n",
    "    <li>three days late, -6.0;</li>\n",
    "    <li>four days late, -8.0;</li>\n",
    "    <li>five days late, -10.0;</li>\n",
    "</ul>\n",
    "\n",
    "<b>Marks</b>: 20% of mark for class. \n",
    "\n",
    "<b>Materials</b>: See [Using Jupyter Notebook and Python page](https://canvas.lms.unimelb.edu.au/courses/126693/pages/python-and-jupyter-notebooks?module_item_id=3950453) on Canvas (under Modules> Coding Resources) for information on the basic setup required for this class, including an iPython notebook viewer.If your iPython notebook doesn't run on the marker's machine, you will lose marks. <b> You should use Python 3</b>.  \n",
    "\n",
    "\n",
    "<b>Evaluation</b>: Your iPython notebook should run end-to-end without any errors in a reasonable amount of time, and you must follow all instructions provided below, including specific implementation requirements and instructions for what needs to be printed (please avoid printing output we don't ask for). You should implement functions for the skeletons listed below. You may implement any number of additional (helper) functions. You should leave the output from running your code in the iPython notebook you submit, to assist with marking. The amount each section is worth is given in parenthesis after the instructions. \n",
    "\n",
    "You will be marked not only on the correctness of your methods, but also the quality and efficiency of your code: in particular, you should be careful to use Python built-in functions and operators when appropriate and pick descriptive variable names that adhere to <a href=\"https://www.python.org/dev/peps/pep-0008/\">Python style requirements</a>. If you think it might be unclear what you are doing, you should comment your code to help the marker make sense of it. We reserve the right to deduct up to 4 marks for unreadable or excessively inefficient code.\n",
    "\n",
    "7 of the marks available for this Project will be assigned to whether the five specified Python functions work in a manner consistent with the materials from COMP90049. Any other implementation will not be directly assessed (except insofar as it is required to make these five functions work correctly).\n",
    "\n",
    "13 of the marks will be assigned to your responses to the questions, in terms of both accuracy and insightfulness. We will be looking for evidence that you have an implementation that allows you to explore the problem, but also that you have thought deeply about the data and the behaviour of the Naive Bayes classifier.\n",
    "\n",
    "<b>Updates</b>: Any major changes to the assignment will be announced via Canvas. Minor changes and clarifications will be announced on the discussion board (Piazza -> Assignments -> A2); we recommend you check it regularly.\n",
    "\n",
    "<b>Academic misconduct</b>: While you may discuss this homework in general terms with other students, it ultimately is still an individual task. Reuse of code or other instances of clear influence will be considered cheating. Please check the <a href=\"https://canvas.lms.unimelb.edu.au/courses/126693/modules#module_734188\">CIS Academic Honesty training</a> for more information. We will be checking submissions for originality and will invoke the University’s <a href=\"http://academichonesty.unimelb.edu.au/policy.html\">Academic Misconduct policy</a> where inappropriate levels of collusion or plagiarism are deemed to have taken place.\n",
    "\n",
    "**IMPORTANT**\n",
    "\n",
    "Please carefully read and fill out the <b>Authorship Declaration</b> form at the bottom of the page. Failure to fill out this form results in the following deductions: \n",
    "<UL TYPE=”square”>\n",
    "<LI>missing Authorship Declaration at the bottom of the page, -10.0\n",
    "<LI>incomplete or unsigned Authorship Declaration at the bottom of the page, -5.0\n",
    "</UL>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: Base code [7 marks]\n",
    "\n",
    "Instructions\n",
    "1. Do **not** shuffle the data set\n",
    "2. Treat the features as nominal and use them as provided (e.g., do **not** convert them to other feature types, such as numeric ones). Implement a Naive Bayes classifier with appropriate likelihood function for the data.\n",
    "3. You should implement the Naive Bayes classifier from scratch. Do **not** use existing implementations/learning algorithms. You must use epsilon smoothing strategy as discussed in the Naive Bayes lecture. \n",
    "4. Apart from the instructions in point 3, you may use libraries to help you with data reading, representation, maths or evaluation.\n",
    "5. Ensure that all and only required information is printed, as indicated in the final three code cells. Failure to adhere to print the required information will result in **[-1 mark]** per case. *(We don't mind details like you print a list or several numbers -- just make sure the information is displayed so that it's easily accessible)*\n",
    "6. Please place the jupyter notebook into the same folder as the input data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function should open a csv file and read the data into a useable format [0.5 mark]\n",
    "import csv\n",
    "def preprocess(filename):\n",
    "    feature_name=[]  #all feature names for file\n",
    "    features=[]      #all feature values for each record\n",
    "    labels=[]        #label for each record\n",
    "    with open(filename) as csv_file:\n",
    "        csv_reader = csv.reader(csv_file, delimiter=',')\n",
    "        line_count = 0\n",
    "        for row in csv_reader:\n",
    "            if line_count == 0:\n",
    "                feature_name=row[1:-1]\n",
    "                line_count += 1\n",
    "            else:\n",
    "                features.append(row[1:-1])     #remove id and label\n",
    "                labels.append(row[-1])\n",
    "                line_count += 1\n",
    "    return feature_name,features,labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function should build a supervised NB model [3 marks]\n",
    "import math\n",
    "def count_label_and_feature_label(features,labels):\n",
    "    label_count=dict()\n",
    "    feature_label_count=dict()\n",
    "    feature_count = dict()\n",
    "    \n",
    "    for feature_values, label in zip(features, labels):\n",
    "        # label count\n",
    "        label_count[label] = label_count.get(label,0.) + 1.\n",
    "        # feature | label count\n",
    "        index = 0   #the index of current feature\n",
    "        for feature_value in feature_values:\n",
    "            key = \"|\".join([str(index), feature_value, label])   #let the \"feature_value | label\" as the key   \n",
    "            feature_label_count[key] = feature_label_count.get(key,0.) + 1.\n",
    "            key2 = \"|\".join([str(index), feature_value])\n",
    "            feature_count[key2] = feature_count.get(key2,0.) + 1.\n",
    "            index += 1\n",
    "    return label_count, feature_label_count, feature_count\n",
    "\n",
    "def train(features,labels):\n",
    "    \n",
    "    #1. count each label and the each feature_value under the each label condition\n",
    "    label_count, feature_label_count, _ = count_label_and_feature_label(features,labels)\n",
    "            \n",
    "    #2. compute the probability of each label and the each feature_value under the each label condition\n",
    "    all_records_len = len(features)\n",
    "    \n",
    "    log_label_p = dict()             #log p(label)\n",
    "    for label,count in label_count.items():\n",
    "        log_label_p[label] = math.log(count/all_records_len,2)\n",
    "    \n",
    "    log_feature2label_p = dict()    #log p(feature|label)\n",
    "    for feature_label, count in feature_label_count.items():\n",
    "        label = feature_label.split(\"|\")[-1]\n",
    "        log_feature2label_p[feature_label] = math.log(count/label_count[label],2)\n",
    "        \n",
    "    return log_label_p, log_feature2label_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function should predict the class for a set of instances, based on a trained model [1.5 marks]\n",
    "import math\n",
    "def predict_one_instance(instance, log_label_p, log_feature2label_p):\n",
    "    epsilon_log = math.log(1e-7,2)\n",
    "    predict_label = None\n",
    "    predict_probas = dict()  #probabilities of each class for one instance\n",
    "    max_predict_probas = -float(\"inf\")   #be used to determine the label\n",
    "    \n",
    "    # Calculate the likelihood corresponding to each class and select the largest as the prediction label\n",
    "    for label, label_p in log_label_p.items():\n",
    "        index = 0\n",
    "        proba = label_p\n",
    "        for fvalue in instance:\n",
    "            key = \"|\".join([str(index), fvalue, label])\n",
    "            proba += log_feature2label_p.get(key, epsilon_log)     #eplison\n",
    "            index+=1\n",
    "        predict_probas[label] = proba\n",
    "        if proba > max_predict_probas:\n",
    "            max_predict_probas = proba\n",
    "            predict_label = label\n",
    "    return predict_label, predict_probas\n",
    "\n",
    "#all instances prediction\n",
    "def predict(test_features, log_label_p, log_feature2label_p):\n",
    "    predict_labels = []\n",
    "    predict_probas = []     #the probabilities of predictions\n",
    "    for instance in test_features:\n",
    "        pred, probas = predict_one_instance(instance,log_label_p, log_feature2label_p )\n",
    "        predict_labels.append(pred)\n",
    "        predict_probas.append(probas)\n",
    "    return predict_labels, predict_probas \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function should evaluate a set of predictions [1 mark]\n",
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
    "def evaluate(labels,predict_labels):\n",
    "    evaluation_list = dict()\n",
    "    evaluation_list[\"overall accuracy\"] = accuracy_score(labels, predict_labels)\n",
    "    evaluation_list[\"overall error\"] = 1 - accuracy_score(labels, predict_labels)\n",
    "    #precision,recall,f-value\n",
    "    #if it is binary class, them compute the values for each class\n",
    "    if len(set(labels)) == 2:\n",
    "        for label in set(labels):\n",
    "            precision, recall, fscore, _ = precision_recall_fscore_support(labels, predict_labels, pos_label=label, average='binary',zero_division=0)\n",
    "            evaluation_list[\"label (\" + label + \")\"+\" precision\"] = precision\n",
    "            evaluation_list[\"label (\" + label + \")\"+\" recall\"] = recall\n",
    "            evaluation_list[\"label (\" + label + \")\"+\" f1_score\"] = fscore\n",
    "    else:\n",
    "        precision, recall, fscore, _ = precision_recall_fscore_support(labels, predict_labels, average='macro',zero_division=0)\n",
    "        evaluation_list[\"macro_precision\"] = precision\n",
    "        evaluation_list[\"macro_recall\"] = recall\n",
    "        evaluation_list[\"macro_f1_score\"] = fscore\n",
    "        \n",
    "        precision, recall, fscore, _ = precision_recall_fscore_support(labels, predict_labels, average='micro',zero_division=0)\n",
    "        evaluation_list[\"micro_precision\"] = precision\n",
    "        evaluation_list[\"micro_recall\"] = recall\n",
    "        evaluation_list[\"micro_f1_score\"] = fscore\n",
    "        \n",
    "        precision, recall, fscore, _ = precision_recall_fscore_support(labels, predict_labels, average='weighted',zero_division=0)\n",
    "        evaluation_list[\"weighted_precision\"] = precision\n",
    "        evaluation_list[\"weighted_recall\"] = recall\n",
    "        evaluation_list[\"weighted_f1_score\"] = fscore\n",
    "    \n",
    "    return evaluation_list      \n",
    "            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bank Marketing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overall accuracy : 0.8874142888741429\n",
      "overall error : 0.11258571112585714\n",
      "label (yes) precision : 0.525\n",
      "label (yes) recall : 0.2418426103646833\n",
      "label (yes) f1_score : 0.3311432325886991\n",
      "label (no) precision : 0.9077318383555244\n",
      "label (no) recall : 0.9715\n",
      "label (no) f1_score : 0.9385339934790485\n",
      "\n",
      "Feature vectors of instances [0, 1, 2]: \n",
      "['unemployed', 'married', 'primary', 'no', 'no', 'no', 'cellular', 'oct', 'unknown']\n",
      "['services', 'married', 'secondary', 'no', 'yes', 'yes', 'cellular', 'may', 'failure']\n",
      "['management', 'single', 'tertiary', 'no', 'yes', 'no', 'cellular', 'apr', 'failure']\n",
      "\n",
      "Number of instances (N):  4521\n",
      "Number of features (F):  9\n",
      "Number of labels (L):  2\n",
      "\n",
      "\n",
      "Predicted class probabilities for instance N-3:  {'no': -9.682803236846793, 'yes': -12.38281143190786}\n",
      "Predicted class for instance N-3:  no\n",
      "\n",
      "Predicted class probabilities for instance N-2:  {'no': -15.321304060024165, 'yes': -16.853856908948405}\n",
      "Predicted class for instance N-2:  no\n",
      "\n",
      "Predicted class probabilities for instance N-1:  {'no': -21.477420991284752, 'yes': -23.49714415332736}\n",
      "Predicted class for instance N-1:  no\n"
     ]
    }
   ],
   "source": [
    "# This cell should act as your \"main\" function where you call the above functions \n",
    "# on the full Bank Marketing data set, and print the evaluation score. [0.33 marks]\n",
    "\n",
    "\n",
    "\n",
    "# First, read in the data and apply your NB model to the Bank Marketing data\n",
    "\n",
    "bank_feature_name,bank_features,bank_labels = preprocess('data/bank-marketing.csv')\n",
    "\n",
    "bank_log_label_p, bank_log_feature2label_p = train(bank_features,bank_labels)\n",
    "\n",
    "bank_predict_labels, bank_predict_probas = predict(bank_features,bank_log_label_p, bank_log_feature2label_p )\n",
    "\n",
    "# Second, print the full evaluation results from the evaluate() function\n",
    "\n",
    "evaluation_list = evaluate(bank_labels,bank_predict_labels)\n",
    "\n",
    "for key,value in evaluation_list.items():\n",
    "    print(key, \":\", value)\n",
    "\n",
    "# Third, print data statistics and model predictions, as instructed below \n",
    "# N is the total number of instances, F the total number of features, L the total number of labels\n",
    "# The \"class probabilities\" may be unnormalized\n",
    "\n",
    "\n",
    "print(\"\\nFeature vectors of instances [0, 1, 2]: \", )\n",
    "print(bank_features[0])\n",
    "print(bank_features[1])\n",
    "print(bank_features[2])\n",
    "print(\"\\nNumber of instances (N): \", len(bank_features))\n",
    "print(\"Number of features (F): \", len(bank_features[0]))\n",
    "print(\"Number of labels (L): \", len(set(bank_labels)))\n",
    "\n",
    "print(\"\\n\\nPredicted class probabilities for instance N-3: \", bank_predict_probas[-3])\n",
    "print(\"Predicted class for instance N-3: \", bank_predict_labels[-3])\n",
    "print(\"\\nPredicted class probabilities for instance N-2: \", bank_predict_probas[-2])\n",
    "print(\"Predicted class for instance N-2: \", bank_predict_labels[-2])\n",
    "print(\"\\nPredicted class probabilities for instance N-1: \", bank_predict_probas[-1])\n",
    "print(\"Predicted class for instance N-1: \", bank_predict_labels[-1])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Student"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overall accuracy : 0.48382126348228044\n",
      "overall error : 0.5161787365177195\n",
      "macro_precision : 0.48358500654751285\n",
      "macro_recall : 0.4763644469373179\n",
      "macro_f1_score : 0.4784911527273963\n",
      "micro_precision : 0.48382126348228044\n",
      "micro_recall : 0.48382126348228044\n",
      "micro_f1_score : 0.48382126348228044\n",
      "weighted_precision : 0.4857742081328605\n",
      "weighted_recall : 0.48382126348228044\n",
      "weighted_f1_score : 0.4834963990558278\n",
      "\n",
      "Feature vectors of instances [0, 1, 2]: \n",
      "['GP', 'F', 'U', 'GT3', 'A', 'high', 'high', 'at_home', 'teacher', 'course', 'mother', 'medium', 'medium', 'none', 'yes', 'no', 'no', 'no', 'yes', 'yes', 'no', 'no', 'good', 'mediocre', 'good', 'very_bad', 'very_bad', 'mediocre', 'four_to_six']\n",
      "['GP', 'F', 'U', 'GT3', 'T', 'low', 'low', 'at_home', 'other', 'course', 'father', 'low', 'medium', 'none', 'no', 'yes', 'no', 'no', 'no', 'yes', 'yes', 'no', 'excellent', 'mediocre', 'mediocre', 'very_bad', 'very_bad', 'mediocre', 'one_to_three']\n",
      "['GP', 'F', 'U', 'LE3', 'T', 'low', 'low', 'at_home', 'other', 'other', 'mother', 'low', 'medium', 'none', 'yes', 'no', 'no', 'no', 'yes', 'yes', 'yes', 'no', 'good', 'mediocre', 'bad', 'bad', 'mediocre', 'mediocre', 'four_to_six']\n",
      "\n",
      "Number of instances (N):  649\n",
      "Number of features (F):  29\n",
      "Number of labels (L):  6\n",
      "\n",
      "\n",
      "Predicted class probabilities for instance N-3:  {'D': -39.74965272410314, 'C': -41.968359570825925, 'B': -42.758799961068235, 'A': -67.76982865504779, 'F': -38.16823464311366, 'A+': -83.45514744355022}\n",
      "Predicted class for instance N-3:  F\n",
      "\n",
      "Predicted class probabilities for instance N-2:  {'D': -42.42130389804595, 'C': -51.04904798961178, 'B': -49.91300910096383, 'A': -55.200274363819865, 'F': -45.31482269753253, 'A+': -79.99891323322394}\n",
      "Predicted class for instance N-2:  D\n",
      "\n",
      "Predicted class probabilities for instance N-1:  {'D': -39.60425705287822, 'C': -48.22469782427844, 'B': -46.9834823089123, 'A': -70.77232628828946, 'F': -42.75640510816708, 'A+': -77.1851320420069}\n",
      "Predicted class for instance N-1:  D\n"
     ]
    }
   ],
   "source": [
    "# This cell should act as your \"main\" function where you call the above functions \n",
    "# on the full Student data set, and print the evaluation score. [0.33 marks]\n",
    "\n",
    "\n",
    "\n",
    "# First, read in the data and apply your NB model to the Student data\n",
    "\n",
    "\n",
    "student_feature_name,student_features,student_labels = preprocess('data/student.csv')\n",
    "\n",
    "student_log_label_p, student_log_feature2label_p = train(student_features,student_labels)\n",
    "\n",
    "student_predict_labels, student_predict_probas = predict(student_features,student_log_label_p, student_log_feature2label_p )\n",
    "\n",
    "# Second, print the full evaluation results from the evaluate() function\n",
    "\n",
    "evaluation_list = evaluate(student_labels,student_predict_labels)\n",
    "\n",
    "for key,value in evaluation_list.items():\n",
    "    print(key, \":\", value)\n",
    "\n",
    "\n",
    "# Third, print data statistics and model predictions, as instructed below \n",
    "# N is the total number of instances, F the total number of features, L the total number of labels\n",
    "# The \"class probabilities\" may be unnormalized\n",
    "\n",
    "print(\"\\nFeature vectors of instances [0, 1, 2]: \", )\n",
    "print(student_features[0])\n",
    "print(student_features[1])\n",
    "print(student_features[2])\n",
    "print(\"\\nNumber of instances (N): \", len(student_features))\n",
    "print(\"Number of features (F): \", len(student_features[0]))\n",
    "print(\"Number of labels (L): \", len(set(student_labels)))\n",
    "\n",
    "print(\"\\n\\nPredicted class probabilities for instance N-3: \", student_predict_probas[-3])\n",
    "print(\"Predicted class for instance N-3: \", student_predict_labels[-3])\n",
    "print(\"\\nPredicted class probabilities for instance N-2: \", student_predict_probas[-2])\n",
    "print(\"Predicted class for instance N-2: \", student_predict_labels[-2])\n",
    "print(\"\\nPredicted class probabilities for instance N-1: \", student_predict_probas[-1])\n",
    "print(\"Predicted class for instance N-1: \", student_predict_labels[-1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obesity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "overall accuracy : 0.7783041212695405\n",
      "overall error : 0.22169587873045948\n",
      "label (not-obese) precision : 0.8682766190998902\n",
      "label (not-obese) recall : 0.694468832309043\n",
      "label (not-obese) f1_score : 0.7717073170731708\n",
      "label (obese) precision : 0.71\n",
      "label (obese) recall : 0.8765432098765432\n",
      "label (obese) f1_score : 0.7845303867403315\n",
      "\n",
      "Feature vectors of instances [0, 1, 2]: \n",
      "['Male', 'yes', 'yes', 'mid', 'high', 'Sometimes', 'yes', 'mid', 'no', 'low-activity', 'mediocre', 'Frequently', 'Public_Transportation']\n",
      "['Male', 'yes', 'yes', 'mid', 'high', 'Sometimes', 'no', 'high', 'no', 'low-activity', 'good', 'Sometimes', 'Public_Transportation']\n",
      "['Male', 'yes', 'yes', 'high', 'high', 'Sometimes', 'no', 'high', 'no', 'low-activity', 'good', 'Sometimes', 'Public_Transportation']\n",
      "\n",
      "Number of instances (N):  2111\n",
      "Number of features (F):  13\n",
      "Number of labels (L):  2\n",
      "\n",
      "\n",
      "Predicted class probabilities for instance N-3:  {'not-obese': -15.885474483636276, 'obese': -24.210437094749295}\n",
      "Predicted class for instance N-3:  not-obese\n",
      "\n",
      "Predicted class probabilities for instance N-2:  {'not-obese': -14.858621707983579, 'obese': -22.000301257692296}\n",
      "Predicted class for instance N-2:  not-obese\n",
      "\n",
      "Predicted class probabilities for instance N-1:  {'not-obese': -10.47624856313417, 'obese': -7.704819272227012}\n",
      "Predicted class for instance N-1:  obese\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# This cell should act as your \"main\" function where you call the above functions \n",
    "# on the full Obesity data set, and print the evaluation score. [0.33 marks]\n",
    "\n",
    "\n",
    "\n",
    "# First, read in the data and apply your NB model to the Obesity data\n",
    "\n",
    "\n",
    "obesity_feature_name,obesity_features,obesity_labels = preprocess('data/obesity.csv')\n",
    "\n",
    "obesity_log_label_p, obesity_log_feature2label_p = train(obesity_features,obesity_labels)\n",
    "\n",
    "obesity_predict_labels, obesity_predict_probas = predict(obesity_features,obesity_log_label_p, obesity_log_feature2label_p )\n",
    "\n",
    "# Second, print the full evaluation results from the evaluate() function\n",
    "\n",
    "evaluation_list = evaluate(obesity_labels,obesity_predict_labels)\n",
    "\n",
    "for key,value in evaluation_list.items():\n",
    "    print(key, \":\", value)\n",
    "\n",
    "\n",
    "# Third, print data statistics and model predictions, as instructed below \n",
    "# N is the total number of instances, F the total number of features, L the total number of labels\n",
    "# The \"class probabilities\" may be unnormalized\n",
    "\n",
    "\n",
    "print(\"\\nFeature vectors of instances [0, 1, 2]: \", )\n",
    "print(obesity_features[0])\n",
    "print(obesity_features[1])\n",
    "print(obesity_features[2])\n",
    "print(\"\\nNumber of instances (N): \", len(obesity_features))\n",
    "print(\"Number of features (F): \", len(obesity_features[0]))\n",
    "print(\"Number of labels (L): \", len(set(obesity_labels)))\n",
    "\n",
    "print(\"\\n\\nPredicted class probabilities for instance N-3: \", obesity_predict_probas[-3])\n",
    "print(\"Predicted class for instance N-3: \", obesity_predict_labels[-3])\n",
    "print(\"\\nPredicted class probabilities for instance N-2: \", obesity_predict_probas[-2])\n",
    "print(\"Predicted class for instance N-2: \", obesity_predict_labels[-2])\n",
    "print(\"\\nPredicted class probabilities for instance N-1: \", obesity_predict_probas[-1])\n",
    "print(\"Predicted class for instance N-1: \", obesity_predict_labels[-1])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 2: Conceptual questions [13 marks]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1: One-R Baseline [3 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Bank-Marketing\n",
      "error rate for each attribute\n",
      "job : 0.11523999115239991\n",
      "marital : 0.11523999115239991\n",
      "education : 0.11523999115239991\n",
      "default : 0.11523999115239991\n",
      "housing : 0.11523999115239991\n",
      "loan : 0.11523999115239991\n",
      "contact : 0.11523999115239991\n",
      "month : 0.11523999115239991\n",
      "poutcome : 0.10705596107055959\n",
      "\n",
      "\n",
      "overall accuracy : 0.8929440389294404\n",
      "overall error : 0.10705596107055959\n",
      "label (yes) precision : 0.6434108527131783\n",
      "label (yes) recall : 0.15930902111324377\n",
      "label (yes) f1_score : 0.2553846153846154\n",
      "label (no) precision : 0.9002732240437158\n",
      "label (no) recall : 0.9885\n",
      "label (no) f1_score : 0.94232602478551\n",
      "\n",
      "Student\n",
      "error rate for each attribute\n",
      "school : 0.6902927580893683\n",
      "sex : 0.6902927580893683\n",
      "address : 0.6902927580893683\n",
      "famsize : 0.6902927580893683\n",
      "Pstatus : 0.6902927580893683\n",
      "Medu : 0.6687211093990755\n",
      "Fedu : 0.6656394453004623\n",
      "Mjob : 0.6810477657935285\n",
      "Fjob : 0.6795069337442219\n",
      "reason : 0.6810477657935285\n",
      "guardian : 0.6902927580893683\n",
      "traveltime : 0.6810477657935285\n",
      "studytime : 0.6733436055469955\n",
      "failures : 0.6810477657935285\n",
      "schoolsup : 0.6902927580893683\n",
      "famsup : 0.6902927580893683\n",
      "paid : 0.6825885978428352\n",
      "activities : 0.6887519260400616\n",
      "nursery : 0.6902927580893683\n",
      "higher : 0.687211093990755\n",
      "internet : 0.6902927580893683\n",
      "romantic : 0.6902927580893683\n",
      "famrel : 0.6841294298921418\n",
      "freetime : 0.6887519260400616\n",
      "goout : 0.6856702619414483\n",
      "Dalc : 0.6902927580893683\n",
      "Walc : 0.6825885978428352\n",
      "health : 0.6825885978428352\n",
      "absences : 0.6795069337442219\n",
      "\n",
      "\n",
      "overall accuracy : 0.33436055469953774\n",
      "overall error : 0.6656394453004623\n",
      "macro_precision : 0.10674084293026231\n",
      "macro_recall : 0.18955008507247315\n",
      "macro_f1_score : 0.1275580702409252\n",
      "micro_precision : 0.33436055469953774\n",
      "micro_recall : 0.33436055469953774\n",
      "micro_f1_score : 0.33436055469953774\n",
      "weighted_precision : 0.17685105019090347\n",
      "weighted_recall : 0.33436055469953774\n",
      "weighted_f1_score : 0.2175167427486735\n",
      "\n",
      "Obesity\n",
      "error rate for each attribute\n",
      "Gender : 0.4604452865940313\n",
      "family_history_with_overweight : 0.364756039791568\n",
      "FAVC : 0.44149692089057324\n",
      "FCVC : 0.42065371861676926\n",
      "NCP : 0.42065371861676926\n",
      "CAEC : 0.39270487920416863\n",
      "SMOKE : 0.4604452865940313\n",
      "CH2O : 0.42349597347228807\n",
      "SCC : 0.4604452865940313\n",
      "FAF : 0.43486499289436287\n",
      "TUE : 0.4604452865940313\n",
      "CALC : 0.4429180483183326\n",
      "MTRANS : 0.4604452865940313\n",
      "\n",
      "\n",
      "overall accuracy : 0.635243960208432\n",
      "overall error : 0.364756039791568\n",
      "label (not-obese) precision : 0.9792207792207792\n",
      "label (not-obese) recall : 0.33099209833187004\n",
      "label (not-obese) f1_score : 0.4947506561679791\n",
      "label (obese) precision : 0.5585168018539977\n",
      "label (obese) recall : 0.9917695473251029\n",
      "label (obese) f1_score : 0.7146034099332839\n"
     ]
    }
   ],
   "source": [
    "# Write additional code here, if necessary (you may insert additional code cells)\n",
    "# You should implement the One-R classifier from scratch. Do not use existing implementations/learning algorithms.\n",
    "# Print the feature name and its corresponding error rate that One-R selects, in addition to any evaluation scores.\n",
    "def one_R_train(features, labels):\n",
    "    model = dict()        #to store the label corresponding each feature value\n",
    "    feature_index = -1     #to store the index of chosen optimal feature\n",
    "    attr2error_rate = dict()           #store the error rate for each attribute\n",
    "    min_error_rate = 1\n",
    "    \n",
    "    #1.count the number of each labels for each attribute value \n",
    "    for attribute_index in range(len(features[0])):\n",
    "        attrv_label_count = dict()     #the number of each class for each attribute\n",
    "        for instance, label in zip(features, labels):\n",
    "            attr_v = instance[attribute_index]\n",
    "            if attr_v not in attrv_label_count:\n",
    "                attrv_label_count[attr_v] = dict()      #The second layer dictionary stores the number of labels for each attribute value   \n",
    "            attrv_label_count[attr_v][label] = attrv_label_count[attr_v].get(label, 0.) + 1.\n",
    "        \n",
    "        model[attribute_index] = dict()\n",
    "        right_count = 0.               #The total number of correctly classified values in a field \n",
    "        for attr_value, value in attrv_label_count.items():\n",
    "            max_label = None\n",
    "            max_count = 0\n",
    "            \n",
    "            for label,count in value.items():\n",
    "                if count>max_count:\n",
    "                    max_count = count\n",
    "                    max_label = label\n",
    "            right_count += max_count    \n",
    "            model[attribute_index][attr_value]=max_label\n",
    "        error_rate = 1-right_count/len(labels)\n",
    "        if error_rate < min_error_rate:\n",
    "            feature_index = attribute_index\n",
    "            min_error_rate = error_rate\n",
    "        attr2error_rate[attribute_index]=error_rate\n",
    "    \n",
    "    return model, feature_index,  attr2error_rate\n",
    "\n",
    "\n",
    "def one_R_predict(model,feature_index,test_features):\n",
    "    predict_labels = []\n",
    "    for instance in test_features:\n",
    "        feature_value = instance[feature_index]\n",
    "        predict_labels.append(model[feature_index][feature_value])\n",
    "    return predict_labels\n",
    "        \n",
    "def print_error_rate_each_attribute(feature_name, attr2error_rate):\n",
    "    print(\"error rate for each attribute\")\n",
    "    for index in range(len(feature_name)):\n",
    "        print(feature_name[index],\":\",attr2error_rate[index])\n",
    "\n",
    "print(\"\\nBank-Marketing\")\n",
    "model, feature_index,  attr2error_rate = one_R_train(bank_features, bank_labels)\n",
    "print_error_rate_each_attribute(bank_feature_name, attr2error_rate)\n",
    "\n",
    "predict_labels = one_R_predict(model,feature_index, bank_features)\n",
    "\n",
    "evaluation_list = evaluate(bank_labels,predict_labels)\n",
    "print(\"\\n\")\n",
    "for key,value in evaluation_list.items():\n",
    "    print(key, \":\", value)\n",
    "\n",
    "print(\"\\nStudent\")\n",
    "model, feature_index,  attr2error_rate = one_R_train(student_features, student_labels)\n",
    "print_error_rate_each_attribute(student_feature_name, attr2error_rate)\n",
    "\n",
    "predict_labels = one_R_predict(model,feature_index, student_features)\n",
    "\n",
    "evaluation_list = evaluate(student_labels,predict_labels)\n",
    "print(\"\\n\")\n",
    "for key,value in evaluation_list.items():\n",
    "    print(key, \":\", value)\n",
    "\n",
    "print(\"\\nObesity\")\n",
    "model, feature_index,  attr2error_rate = one_R_train(obesity_features, obesity_labels)\n",
    "print_error_rate_each_attribute(obesity_feature_name, attr2error_rate)\n",
    "\n",
    "predict_labels = one_R_predict(model,feature_index, obesity_features)\n",
    "\n",
    "evaluation_list = evaluate(obesity_labels,predict_labels)\n",
    "print(\"\\n\")\n",
    "for key,value in evaluation_list.items():\n",
    "    print(key, \":\", value)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Provide your text answer to **Question 1.b** of 100-150 words in this cell.\n",
    "\n",
    "On the Student and Obesity datasets, the Naive Bayes classifier significantly beats the One-R baseline model, whereas on the Bank-Marketing dataset it achieves performance equivalent to the One-R baseline model. The results show that Student and Obesity datasets need more complex models to predict, while bank-marketing can predict a good result by using an optimal attribute, and this optimal attribute plays a relatively high decisive role in label. This is also consistent with our common sense. In life, whether time deposit is closely related to historical data. If customers did not choose deposit in the last promotion, they will basically choose \"No\" this time. However, the scores of students and obesity will be affected by many factors. For example, students are smart and can get high grades even though they are absent from school; despite the intake of a lot of high-calorie food, but a large amount of exercise, also won't cause obesity.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2: Evaluation strategy [3 marks] \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Bank-Marketing\n",
      "overall accuracy : 0.8852043709969197\n",
      "overall error : 0.11479562900308023\n",
      "label (yes) precision : 0.5079986666971229\n",
      "label (yes) recall : 0.23198192256300573\n",
      "label (yes) f1_score : 0.3180161501527717\n",
      "label (no) precision : 0.9065500278870333\n",
      "label (no) recall : 0.9702459716137972\n",
      "label (no) f1_score : 0.9373096621830734\n",
      "\n",
      "Student\n",
      "overall accuracy : 0.3420751341681574\n",
      "overall error : 0.6579248658318426\n",
      "macro_precision : 0.26890998274872463\n",
      "macro_recall : 0.2534721601352687\n",
      "macro_f1_score : 0.24656574059692754\n",
      "micro_precision : 0.3420751341681574\n",
      "micro_recall : 0.3420751341681574\n",
      "micro_f1_score : 0.3420751341681574\n",
      "weighted_precision : 0.35174332208689074\n",
      "weighted_recall : 0.3420751341681574\n",
      "weighted_f1_score : 0.3314954007815428\n",
      "\n",
      "Obesity\n",
      "overall accuracy : 0.7759167759066923\n",
      "overall error : 0.22408322409330778\n",
      "label (not-obese) precision : 0.865040466413104\n",
      "label (not-obese) recall : 0.6938426817193346\n",
      "label (not-obese) f1_score : 0.7698149404128731\n",
      "label (obese) precision : 0.7082217110828897\n",
      "label (obese) recall : 0.871831818890331\n",
      "label (obese) f1_score : 0.7814098990034528\n"
     ]
    }
   ],
   "source": [
    "# Write additional code here, if necessary (you may insert additional code cells)\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "def cross_validation(features, labels, k=5):\n",
    "    # prepare cross validation\n",
    "    kfold = KFold(k)\n",
    "    k_evaluations_list=[]\n",
    "    # enumerate splits\n",
    "    for train_index, test_index in kfold.split(features):\n",
    "        # prepare the train and test set\n",
    "        train_feature_set = [features[index] for index in train_index]\n",
    "        train_label_set = [labels[index] for index in train_index]\n",
    "        test_feature_set = [features[index] for index in test_index]\n",
    "        test_label_set = [labels[index] for index in test_index]\n",
    "\n",
    "        #train, predict and evaluation   \n",
    "        log_label_p, log_feature2label_p = train(train_feature_set,train_label_set)\n",
    "        predict_labels, predict_probas = predict(test_feature_set,log_label_p, log_feature2label_p)\n",
    "        evaluation_list = evaluate(test_label_set, predict_labels)\n",
    "        k_evaluations_list.append(evaluation_list)\n",
    "\n",
    "    # compute the average of k folds\n",
    "    result_list = dict()\n",
    "    for evaluation in k_evaluations_list:\n",
    "        for key,value in evaluation.items():\n",
    "            result_list[key] = result_list.get(key,0.) + value/k\n",
    "    \n",
    "    return result_list\n",
    "\n",
    "print(\"\\nBank-Marketing\")\n",
    "evaluation_list = cross_validation(bank_features,bank_labels)\n",
    "for key,value in evaluation_list.items():\n",
    "    print(key, \":\", value)\n",
    "    \n",
    "print(\"\\nStudent\")\n",
    "evaluation_list = cross_validation(student_features,student_labels)\n",
    "for key,value in evaluation_list.items():\n",
    "    print(key, \":\", value)\n",
    "\n",
    "print(\"\\nObesity\")\n",
    "evaluation_list = cross_validation(obesity_features,obesity_labels)\n",
    "for key,value in evaluation_list.items():\n",
    "    print(key, \":\", value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Provide your text answer to **Question 2** 100-150 words in this cell.\n",
    "\n",
    "On the datasets for Bank-Marketing and Obesity, cross-validation performs almost as well as the No train-test splitting technique, but poorly on the Student dataset. \n",
    "The reason why the No train-test split method is better is that the test is performed on the training set and the distribution in the test data is the same as the distribution in the training data. All the test data is seen during training, just like the human exam questions are all reviewed knowledge points, so it is easy to get better performance. As for cross-validation, it is likely that the distribution of the test set is different from that of the training set, and the test set is the data never seen, so its performance is not as high as the former.\n",
    "Back to the example, Bank-marketing and Obesity are binary class datasets, which can be easily stratified, so their performance is similar to the No train-test split. However, it is difficult to stratify the Student datasets with multiple categories, so its performance is degraded."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3: Feature Selection and Naive Bayes Assumptions [3 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write additional code here, if necessary (you may insert additional code cells)\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "def get_mi(features,labels):\n",
    "    features_mi = [] \n",
    "    \n",
    "    #1. count each label and the each feature_value under the each label condition\n",
    "    label_count, feature_label_count, feature_count = count_label_and_feature_label(features,labels)\n",
    "    \n",
    "    #2. calculate the mi\n",
    "    total_count = len(labels)\n",
    "    for index in range(len(features[0])):\n",
    "        current_mi = 0\n",
    "        for key, count in feature_label_count.items():\n",
    "            f_i, f_v, label = key.split(\"|\")\n",
    "            feature_v = \"|\".join([f_i,f_v])\n",
    "            if f_i == str(index):\n",
    "                p_mi = (count*total_count)/(label_count[label]*feature_count[feature_v])\n",
    "                current_mi += (count/total_count)*math.log(p_mi,2)\n",
    "        features_mi.append(current_mi)\n",
    "    return features_mi\n",
    "\n",
    "\n",
    "def plot(filename,features,labels,feature_name):\n",
    "    print(\"\\n\")\n",
    "    mi = get_mi(features, labels)\n",
    "    attribute = list(range(len(feature_name)))\n",
    "    plt.figure(figsize=(20,8))\n",
    "    plt.plot(attribute, mi, color = 'orange')\n",
    "    plt.xticks(attribute, feature_name,rotation=30,fontsize=11)\n",
    "    plt.xlabel('Feature Name',fontsize=18)\n",
    "    plt.ylabel('Mutual Information',fontsize=18)\n",
    "    plt.title(filename,fontsize=20)\n",
    "\n",
    "    for x,y in zip(attribute, mi):\n",
    "        plt.text(x, y+0.0005, '%.4f' % y, ha='center', va= 'bottom')\n",
    "    plt.show()\n",
    "\n",
    "plot(\"Bank-Marketing\",bank_features,bank_labels,bank_feature_name)\n",
    "plot(\"Student\",student_features,student_labels,student_feature_name)\n",
    "plot(\"Obesity\",obesity_features,obesity_labels,obesity_feature_name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Provide your text answer to **Question 3.a** of 100-150 words in this cell.\n",
    "\n",
    "Different features play different roles in the classification task.\n",
    "\n",
    "As for Bank-Marketing, it is found that the attribute \"poutcome\" is the most important feature among all features, because its mutual information is the highest. Besides, the attribute \"default\" nearly has no impact on the task, beacuse its mutual information is close to 0, which means that the it is irrelevant to the classification.\n",
    "\n",
    "As for Student, it is found that the attribute \"features\" is the most important feature among all features, because its mutual information is the highest. Besides, the attributes \"romantic\",\"famsize\" and \"Patatus\" nearly have no impact on the task, beacuse their mutual information is close to 0, which means that the they are irrelevant to the classification.\n",
    "\n",
    "As for Obesity, it is found that the attributes \"famility_history_with_overweight\" and \"CAEC\" are more important than others, because their mutual information are higher. Besides, the attributes \"Gender\" and \"SMOKE\" nearly have no impact on the task, beacuse their mutual information is close to 0, which means that the they are irrelevant to the classification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Provide your text answer to **Question 3.b** of 100-150 words in this cell.\n",
    "\n",
    "Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. This is because the probability of  𝑝(𝑥1,𝑥2,⋯,𝑥𝑚|𝑦)  is infeasible where  𝑦  is the class and  𝑥𝑖  is the  𝑖 th feature. But with this assumption,  𝑝(𝑥1,𝑥2,⋯,𝑥𝑚|𝑦)≈∏𝑚𝑖=1𝑝(𝑥𝑖|𝑦)  and  𝑝(𝑥𝑖|𝑦)  is feasible.\n",
    "\n",
    "As for Bank-Marketing, if the class is no, month (last contact month of year) dose not depend on poutcome(outcome of the previous marketing campaign).\n",
    "As for Student, if the class is C, Pstatus (parents cohabitation status) dose not depend on famrel (quality of family relationships).\n",
    "As for Obesity, if the class is obese, NCP (Number of main meals) dose not depend on CAEC (Consumption of food between meals).\n",
    "But these are against common sense."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4: Feature Selection and Ethics [4 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "bank-marking\n",
      "overall accuracy : 0.8823269188232692\n",
      "overall error : 0.11767308117673081\n",
      "label (yes) precision : 0.43956043956043955\n",
      "label (yes) recall : 0.07677543186180422\n",
      "label (yes) f1_score : 0.130718954248366\n",
      "label (no) precision : 0.8914221218961625\n",
      "label (no) recall : 0.98725\n",
      "label (no) f1_score : 0.9368920521945434\n",
      "\n",
      "Student\n",
      "overall accuracy : 0.3913713405238829\n",
      "overall error : 0.6086286594761171\n",
      "macro_precision : 0.3610164925261712\n",
      "macro_recall : 0.299125783737285\n",
      "macro_f1_score : 0.29799933710979204\n",
      "micro_precision : 0.3913713405238829\n",
      "micro_recall : 0.3913713405238829\n",
      "micro_f1_score : 0.3913713405238829\n",
      "weighted_precision : 0.3717203883372989\n",
      "weighted_recall : 0.3913713405238829\n",
      "weighted_f1_score : 0.36319812042619426\n",
      "\n",
      "Obesity\n",
      "overall accuracy : 0.7437233538607295\n",
      "overall error : 0.2562766461392705\n",
      "label (not-obese) precision : 0.8235930735930735\n",
      "label (not-obese) recall : 0.6681299385425812\n",
      "label (not-obese) f1_score : 0.7377605428986913\n",
      "label (obese) precision : 0.6815501263689975\n",
      "label (obese) recall : 0.8323045267489712\n",
      "label (obese) f1_score : 0.7494210282538212\n"
     ]
    }
   ],
   "source": [
    "# Write additional code here, if necessary (you may insert additional code cells)\n",
    "print(\"\\nbank-marking\")\n",
    "without_ethics_bank_features = []\n",
    "for feature in bank_features:\n",
    "    without_ethics_bank_features.append([feature[idx] for idx in [1,3,4,5,7]])\n",
    "bank_log_label_p, bank_log_feature2label_p = train(without_ethics_bank_features,bank_labels)\n",
    "bank_predict_labels, bank_predict_probas = predict(without_ethics_bank_features,bank_log_label_p, bank_log_feature2label_p )\n",
    "evaluation_list = evaluate(bank_labels,bank_predict_labels)\n",
    "for key,value in evaluation_list.items():\n",
    "    print(key, \":\", value)\n",
    "    \n",
    "print(\"\\nStudent\")\n",
    "without_ethics_student_features = []\n",
    "for feature in student_features:\n",
    "    without_ethics_student_features.append([feature[idx] for idx in [12, 13, 24, 25, 26, 28]])\n",
    "student_log_label_p, student_log_feature2label_p = train(without_ethics_student_features,student_labels)\n",
    "student_predict_labels, student_predict_probas = predict(without_ethics_student_features,student_log_label_p, student_log_feature2label_p )\n",
    "evaluation_list = evaluate(student_labels,student_predict_labels)\n",
    "for key,value in evaluation_list.items():\n",
    "    print(key, \":\", value)\n",
    "    \n",
    "print(\"\\nObesity\")\n",
    "without_ethics_obesity_features = []\n",
    "for feature in obesity_features:\n",
    "    without_ethics_obesity_features.append([feature[idx] for idx in range(2,13)])\n",
    "obesity_log_label_p, obesity_log_feature2label_p = train(without_ethics_obesity_features,obesity_labels)\n",
    "obesity_predict_labels, obesity_predict_probas = predict(without_ethics_obesity_features,obesity_log_label_p,obesity_log_feature2label_p )\n",
    "evaluation_list = evaluate(obesity_labels,obesity_predict_labels)\n",
    "for key,value in evaluation_list.items():\n",
    "    print(key, \":\", value)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Provide your text answer to **Question 4.a** of 100-150 words in this cell.\n",
    "\n",
    "In the student data set, gender as a feature plays a certain role in classification. According to the results of question 3, the mutual information of \"sex\" is the same as that of \"activity\"(whether they participate in extracurricular activities), indicating that they have the same effect on classification, which obviously results in gender discrimination. What's more, a student's family history, such as their \"guardian\", is taken into account while grading them. It implies that applicants with poor backgrounds but strong academic records would not be admitted, which is unfair.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Provide your text answer to **Question 4.b** of 100-150 words in this cell.\n",
    "\n",
    "The performance of a classifier with cleaned features is worse than the one with full features. This indicates that although the removed attributes are not ethical, they are closely related to the classification results, and there will be less features after removal. For example, students' grades are closely related to whether their parents provide educational support. Students who are provided with support are more likely to get high grades."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Provide your text answer to **Question 4.c** of 100-150 words in this cell.\n",
    "\n",
    "If all ethical features are removed, it is still not guaranteed to be a fair classifier. This is because the data set itself collects limited features, and there are many influencing factors of a person's performance, which are also different from person to person. We cannot find out all the factors and collect them, so it is unfair to some people with special circumstances. For example, a naturally intelligent student may spend very little time studying, and although he can easily handle exams, he get low grade.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Authorship Declaration</b>:\n",
    "\n",
    "   (1) I certify that the program contained in this submission is completely\n",
    "   my own individual work, except where explicitly noted by comments that\n",
    "   provide details otherwise.  I understand that work that has been developed\n",
    "   by another student, or by me in collaboration with other students,\n",
    "   or by non-students as a result of request, solicitation, or payment,\n",
    "   may not be submitted for assessment in this subject.  I understand that\n",
    "   submitting for assessment work developed by or in collaboration with\n",
    "   other students or non-students constitutes Academic Misconduct, and\n",
    "   may be penalized by mark deductions, or by other penalties determined\n",
    "   via the University of Melbourne Academic Honesty Policy, as described\n",
    "   at https://academicintegrity.unimelb.edu.au.\n",
    "\n",
    "   (2) I also certify that I have not provided a copy of this work in either\n",
    "   softcopy or hardcopy or any other form to any other student, and nor will\n",
    "   I do so until after the marks are released. I understand that providing\n",
    "   my work to other students, regardless of my intention or any undertakings\n",
    "   made to me by that other student, is also Academic Misconduct.\n",
    "\n",
    "   (3) I further understand that providing a copy of the assignment\n",
    "   specification to any form of code authoring or assignment tutoring\n",
    "   service, or drawing the attention of others to such services and code\n",
    "   that may have been made available via such a service, may be regarded\n",
    "   as Student General Misconduct (interfering with the teaching activities\n",
    "   of the University and/or inciting others to commit Academic Misconduct).\n",
    "   I understand that an allegation of Student General Misconduct may arise\n",
    "   regardless of whether or not I personally make use of such solutions\n",
    "   or sought benefit from such actions.\n",
    "\n",
    "   <b>Signed by</b>: ZHUOYA ZHOU\n",
    "   \n",
    "   <b>Dated</b>: 31/08/2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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